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It seeks to identify the best practices for extracting value from professional medical chats (PMCs) and assess the impact of AI on patient outcomes and healthcare systems, emphasizing the integration of ethical and responsible AI practices. We conducted an extensive systematic literature review using the Web of Science Core Collection. Searches encompassed English-language articles published between January 2019 and July 2023 using keywords related to AI, machine learning, natural language processing, and physician communication. Of the 247 articles screened, 13 met the inclusion criteria given their in-depth analysis of AI in healthcare communication, methodological soundness, and relevance to clinical outcomes. The review provides insights into interprofessional communication dynamics, the advancement of NLP and deep learning in medical dialogues, and strategies for effective human-machine collaboration. Ethical considerations and the need for transparency in AI applications are key to these central findings. This study highlights the untapped potential of physician-generated real-world data in creating value for healthcare ecosystems. It advocates for a multidisciplinary strategy encompassing communication, education, and collaboration to advance AI in healthcare responsibly. Moreover, it suggests that by combining existing techniques in the AI discipline, including neural networks, generative AI, and genetic algorithms, as well as keeping a \u201cphysician in the loop\u201d when building AI systems, we can have a significant impact on healthcare delivery and medical research.<\/jats:p>","DOI":"10.1007\/s11063-025-11725-1","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T11:44:58Z","timestamp":1737373498000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Value Creation for Healthcare Ecosystems through Artificial Intelligence Applied to Physician-to-Physician Communication: A Systematic Review"],"prefix":"10.1007","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6578-1467","authenticated-orcid":false,"given":"Beny","family":"Rubinstein","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1941-3983","authenticated-orcid":false,"given":"Sergio","family":"Matos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"key":"11725_CR1","doi-asserted-by":"publisher","unstructured":"Zahlan A, Ranjan RP, Hayes D (2023) Artificial intelligence innovation in healthcare: literature review, exploratory analysis, and future research. 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